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Therefore, they heavily rely on the quality of video segmentation maps, which are often unstable, leading to unreliable reconstruction results. To overcome this challenge, our key idea is to represent the decomposed 4D scene with the Freetime FeatureGS and design a streaming feature learning strategy to accurately recover it from per-image segmentation maps, eliminating the need for video segmentation. Freetime FeatureGS models the dynamic scene as a set of Gaussian primitives with learnable features and linear motion ability, allowing them to move to neighboring regions over time. We apply a contrastive loss to Freetime FeatureGS, forcing primitive features to be close or far apart based on whether their projections belong to the same instance in the 2D segmentation map. As our Gaussian primitives can move across time, it naturally extends the feature learning to the temporal dimension, achieving 4D segmentation. Furthermore, we sample observations for training in a temporally ordered manner, enabling the streaming propagation of features over time and effectively avoiding local minima during the optimization process. Experimental results on several datasets show that the reconstruction quality of our method outperforms recent methods by a large margin.<\/jats:p>","DOI":"10.1145\/3763343","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T17:15:39Z","timestamp":1764868539000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Split4D: Decomposed 4D Scene Reconstruction Without Video Segmentation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3433-3733","authenticated-orcid":false,"given":"Yongzhen","family":"Hu","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"},{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9496-3739","authenticated-orcid":false,"given":"Yihui","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&amp;CG, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5862-3268","authenticated-orcid":false,"given":"Haotong","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&amp;CG, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7565-7839","authenticated-orcid":false,"given":"Yifan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&amp;CG, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0050-3989","authenticated-orcid":false,"given":"Junting","family":"Dong","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5891-7108","authenticated-orcid":false,"given":"Yifu","family":"Deng","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9139-5190","authenticated-orcid":false,"given":"Xinyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5541-0449","authenticated-orcid":false,"given":"Fan","family":"Jia","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2662-0334","authenticated-orcid":false,"given":"Hujun","family":"Bao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&amp;CG, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1926-5597","authenticated-orcid":false,"given":"Xiaowei","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&amp;CG, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6546-4525","authenticated-orcid":false,"given":"Sida","family":"Peng","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"European Conference on Computer Vision. 321\u2013335","author":"Bae Jeongmin","year":"2024","unstructured":"Jeongmin Bae, Seoha Kim, Youngsik Yun, Hahyun Lee, Gun Bang, and Youngjung Uh. 2024. 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